Artificial Intelligence in the Fight for Data Loss Prevention

In an age where vast quantities of sensitive information are stored online, cybersecurity measures are more important than ever before.

It is crucial for companies to protect their data, for safeguarding privacy and to avoid data loss. Data breaches can have drastic consequences. And as criminals become more innovative, companies must evolve to avoid being attacked.

Data Loss Protection (DLP) is an emerging technology which monitors, identifies, and protects data. It involves framed and mandated policies, which are designed to prevent unwanted parties accessing confidential data.

DLP offers a powerful measure of control and protection, and it can be applied across various industries. There are many exit points for the technology, including web browsers, removable devices, and instant messengers, which leverage deployable solutions like whitelists, fingerprinting, and thresholds.

Artificial Intelligence & DLP

Artificial Intelligence (AI) seems poised to transform the business world as we know it, and there is scope for it to transcend DLP solutions too. This will occur through tackling losses early on by capitalizing on intelligent algorithms. Learning manipulation patterns and data transfer will develop self-repair capabilities alongside control techniques and self-adjustment.

The Two Main Approaches

Implementing AI for data protection can be approached in two ways. Vendors can either design their own AI systems from the big data they cultivate, or AI providers themselves can customize technology to be used in different security contexts.

With such a wide scope to consider there is arguably room for both methods, especially considering AI experts can apply their extensive knowledge to create highly intelligent algorithms. On the other side of the equation, in-house AI will revolutionize operations that are tailored based on individual needs and requirements.

Data Contextualisation

Context-aware protection is a rising concept which could make a real impact in the near future. Current methods do not analyze external factors, such as when and where high-ranking staff will access data.

Data security is a broad spectrum which isn’t a one-size-fits-all approach, but contextualization is proposed as a solution for the associated gray areas. Process analysis will reshape risk handling, using techniques revolving around data and action. Additional context is provided by receiving a snapshot of the world at a given moment.

Promising technologies like geolocation can enrich AI and DLP interactions, providing more advanced data sets to analyze security violations. Beacons can be used to improve the experience further, and companies would be sensible to communicate the data collected as part of their DLP solutions. When these are context aware, there’s a greater scope to avoid privacy concerns.

Organizations will need to educate their employees, introducing a timeline for implementation. This is no easy task, but when gradually introduced, staff are more likely to embrace the unknown. As humans we naturally resist change, so it’s essential you derive feedback from stakeholders, creating clear communication channels for them to understand implementation goals. This promotes a smooth transition where your invested parties’ needs are considered.

Context-aware DLP solutions are poised to benefit from advancing technology, especially those which differentiate between sensitive and insensitive data. As we progress into unchartered waters, it’s intriguing to consider how these technologies will impact business landscapes going forward.


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